To relief the extra computation cost, some works introduce human prior to improve label assignment. FreeAnchor [1]} proposes one detector that guarantees at least one anchor's prediction close to the ground-truth for each object. GuideAnchoring [2]} and MetaAnchor [3]} try to dynamically change the shape of anchors to fit various distributions of objects. The aforementioned methods, whether prior-related (e.g., GuideAnchoring) or instance-related (e.g., FreeAnchor), ignore the inconsistent supervision problem existing during the assignment process. Therefore, they are still not free from the confusion caused by "similar proposals, different regression targets", and thus bring about false regression results during inference. Compared to their, ours is not only dynamic and flexible, but also ensures the consistency of matching, so it better answers the question of "which to match" and thus shows further improvement for accurate pedestrian localization. On top of above, combined with depth estimation (z axis), our work is also the first attempt that tries to solve the assignment problem from a 3D perspective compared with other 2D assignment (x-y axis), e.g. ATSS [4]} or PAA [5]}.
